Machine Learning
Understand machine learning concepts, model development, training, evaluation, and how models reach production.
This series follows a model through its life: framing the problem, preparing data, training, evaluating honestly, deploying and monitoring.
It is the practical companion to AI Fundamentals. No code is required. References point to widely used course material and documentation.
What You’ll Learn
Framing defines what the model predicts (the target), for which unit (a customer, an order, a document), and how success is measured. It also sets a baseline, often a simple rule or average, that any model must beat to be worthwhile.
If you cannot say exactly what the model should predict and how you will know it helped, the project is not ready to start.
Google’s ML Crash Course recommends stating the problem, the ideal outcome and the success metric before choosing any algorithm.